Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations42896
Missing cells52072
Missing cells (%)7.1%
Duplicate rows906
Duplicate rows (%)2.1%
Total size in memory12.9 MiB
Average record size in memory315.7 B

Variable types

Numeric11
Categorical4
Text2

Alerts

type has constant value "audio_features" Constant
Dataset has 906 (2.1%) duplicate rowsDuplicates
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
duration_ms is highly overall correlated with instrumentalnessHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
instrumentalness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with energyHigh correlation
time_signature is highly imbalanced (84.3%) Imbalance
loudness has 9170 (21.4%) missing values Missing
song_name has 21085 (49.2%) missing values Missing
title has 21817 (50.9%) missing values Missing
key has 3520 (8.2%) zeros Zeros
instrumentalness has 11473 (26.7%) zeros Zeros

Reproduction

Analysis started2025-03-19 16:14:43.582273
Analysis finished2025-03-19 16:14:49.950826
Duration6.37 seconds
Software versionydata-profiling vv4.15.0
Download configurationconfig.json

Variables

danceability
Real number (ℝ)

Distinct921
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63933611
Minimum0.0651
Maximum0.988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.3 KiB
2025-03-19T12:14:49.993535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0651
5-th percentile0.378
Q10.524
median0.646
Q30.766
95-th percentile0.878
Maximum0.988
Range0.9229
Interquartile range (IQR)0.242

Descriptive statistics

Standard deviation0.15658499
Coefficient of variation (CV)0.24491811
Kurtosis-0.48618557
Mean0.63933611
Median Absolute Deviation (MAD)0.121
Skewness-0.265132
Sum27424.962
Variance0.024518859
MonotonicityNot monotonic
2025-03-19T12:14:50.048771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.804 193
 
0.4%
0.808 166
 
0.4%
0.8 154
 
0.4%
0.802 153
 
0.4%
0.805 150
 
0.3%
0.803 150
 
0.3%
0.809 141
 
0.3%
0.81 136
 
0.3%
0.807 134
 
0.3%
0.792 126
 
0.3%
Other values (911) 41393
96.5%
ValueCountFrequency (%)
0.0651 2
< 0.1%
0.0753 1
< 0.1%
0.0891 2
< 0.1%
0.0979 1
< 0.1%
0.106 2
< 0.1%
0.111 1
< 0.1%
0.116 1
< 0.1%
0.119 1
< 0.1%
0.121 1
< 0.1%
0.123 1
< 0.1%
ValueCountFrequency (%)
0.988 3
< 0.1%
0.985 4
< 0.1%
0.984 1
 
< 0.1%
0.983 2
< 0.1%
0.982 1
 
< 0.1%
0.981 1
 
< 0.1%
0.98 2
< 0.1%
0.979 2
< 0.1%
0.978 3
< 0.1%
0.977 2
< 0.1%

energy
Real number (ℝ)

High correlation 

Distinct955
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76256045
Minimum0.000243
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.3 KiB
2025-03-19T12:14:50.107634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.000243
5-th percentile0.425
Q10.632
median0.803
Q30.923
95-th percentile0.982
Maximum1
Range0.999757
Interquartile range (IQR)0.291

Descriptive statistics

Standard deviation0.18369117
Coefficient of variation (CV)0.24088736
Kurtosis-0.20130099
Mean0.76256045
Median Absolute Deviation (MAD)0.135
Skewness-0.73716286
Sum32710.793
Variance0.033742448
MonotonicityNot monotonic
2025-03-19T12:14:50.166805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.977 191
 
0.4%
0.953 172
 
0.4%
0.988 171
 
0.4%
0.97 170
 
0.4%
0.956 169
 
0.4%
0.924 165
 
0.4%
0.963 162
 
0.4%
0.98 159
 
0.4%
0.951 159
 
0.4%
0.942 158
 
0.4%
Other values (945) 41220
96.1%
ValueCountFrequency (%)
0.000243 2
< 0.1%
0.000602 1
< 0.1%
0.0127 1
< 0.1%
0.0148 1
< 0.1%
0.0223 1
< 0.1%
0.0279 2
< 0.1%
0.0297 1
< 0.1%
0.0321 1
< 0.1%
0.0393 1
< 0.1%
0.0453 1
< 0.1%
ValueCountFrequency (%)
1 5
 
< 0.1%
0.999 22
 
0.1%
0.999 3
 
< 0.1%
0.998 87
0.2%
0.997 94
0.2%
0.996 100
0.2%
0.995 123
0.3%
0.994 144
0.3%
0.994 4
 
< 0.1%
0.993 133
0.3%

key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3685658
Minimum0
Maximum11
Zeros3520
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size335.3 KiB
2025-03-19T12:14:50.213588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.6649926
Coefficient of variation (CV)0.68267629
Kurtosis-1.365958
Mean5.3685658
Median Absolute Deviation (MAD)4
Skewness-0.00095694629
Sum230290
Variance13.432171
MonotonicityNot monotonic
2025-03-19T12:14:50.255258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 7641
17.8%
7 4332
10.1%
11 4197
9.8%
6 3767
8.8%
0 3520
8.2%
8 3405
7.9%
10 3294
7.7%
9 3293
7.7%
2 3093
7.2%
5 3047
 
7.1%
Other values (2) 3307
7.7%
ValueCountFrequency (%)
0 3520
8.2%
1 7641
17.8%
2 3093
7.2%
3 910
 
2.1%
4 2397
 
5.6%
5 3047
 
7.1%
6 3767
8.8%
7 4332
10.1%
8 3405
7.9%
9 3293
7.7%
ValueCountFrequency (%)
11 4197
9.8%
10 3294
7.7%
9 3293
7.7%
8 3405
7.9%
7 4332
10.1%
6 3767
8.8%
5 3047
7.1%
4 2397
5.6%
3 910
 
2.1%
2 3093
7.2%

loudness
Real number (ℝ)

High correlation  Missing 

Distinct11257
Distinct (%)33.4%
Missing9170
Missing (%)21.4%
Infinite0
Infinite (%)0.0%
Mean-6.4638994
Minimum-32.929
Maximum3.148
Zeros1
Zeros (%)< 0.1%
Negative33562
Negative (%)78.2%
Memory size335.3 KiB
2025-03-19T12:14:50.303850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-32.929
5-th percentile-11.626
Q1-8.165
median-6.2285
Q3-4.52
95-th percentile-2.00625
Maximum3.148
Range36.077
Interquartile range (IQR)3.645

Descriptive statistics

Standard deviation2.9381711
Coefficient of variation (CV)-0.45455087
Kurtosis1.4448485
Mean-6.4638994
Median Absolute Deviation (MAD)1.817
Skewness-0.62893063
Sum-218001.47
Variance8.6328493
MonotonicityNot monotonic
2025-03-19T12:14:50.361516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.142 19
 
< 0.1%
-6.186 17
 
< 0.1%
-5.892 16
 
< 0.1%
-5.943 16
 
< 0.1%
-5.572 15
 
< 0.1%
-5.334 15
 
< 0.1%
-5.531 15
 
< 0.1%
-4.162 15
 
< 0.1%
-7.641 15
 
< 0.1%
-5.545 14
 
< 0.1%
Other values (11247) 33569
78.3%
(Missing) 9170
 
21.4%
ValueCountFrequency (%)
-32.929 1
< 0.1%
-26.172 1
< 0.1%
-26.113 1
< 0.1%
-24.694 1
< 0.1%
-24.563 1
< 0.1%
-24.203 1
< 0.1%
-23.577 1
< 0.1%
-23.39 1
< 0.1%
-23.385 1
< 0.1%
-23.035 1
< 0.1%
ValueCountFrequency (%)
3.148 1
< 0.1%
2.499 1
< 0.1%
2.363 1
< 0.1%
2.185 1
< 0.1%
1.949 1
< 0.1%
1.877 1
< 0.1%
1.851 1
< 0.1%
1.85 1
< 0.1%
1.833 1
< 0.1%
1.792 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
1
23571 
0
19325 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42896
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23571
54.9%
0 19325
45.1%

Length

2025-03-19T12:14:50.412716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T12:14:50.456509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 23571
54.9%
0 19325
45.1%

Most occurring characters

ValueCountFrequency (%)
1 23571
54.9%
0 19325
45.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23571
54.9%
0 19325
45.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23571
54.9%
0 19325
45.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23571
54.9%
0 19325
45.1%

speechiness
Real number (ℝ)

Distinct1474
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13644625
Minimum0.0227
Maximum0.946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.3 KiB
2025-03-19T12:14:50.505323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0227
5-th percentile0.0348
Q10.0491
median0.0754
Q30.193
95-th percentile0.398
Maximum0.946
Range0.9233
Interquartile range (IQR)0.1439

Descriptive statistics

Standard deviation0.12597627
Coefficient of variation (CV)0.92326664
Kurtosis2.889166
Mean0.13644625
Median Absolute Deviation (MAD)0.0347
Skewness1.6715108
Sum5852.9984
Variance0.015870021
MonotonicityNot monotonic
2025-03-19T12:14:50.560695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.102 123
 
0.3%
0.106 116
 
0.3%
0.105 115
 
0.3%
0.109 113
 
0.3%
0.111 112
 
0.3%
0.11 110
 
0.3%
0.113 106
 
0.2%
0.107 104
 
0.2%
0.114 100
 
0.2%
0.124 96
 
0.2%
Other values (1464) 41801
97.4%
ValueCountFrequency (%)
0.0227 2
< 0.1%
0.0232 2
< 0.1%
0.0235 1
 
< 0.1%
0.0238 1
 
< 0.1%
0.0239 1
 
< 0.1%
0.0241 1
 
< 0.1%
0.0242 3
< 0.1%
0.0243 1
 
< 0.1%
0.0244 2
< 0.1%
0.0245 1
 
< 0.1%
ValueCountFrequency (%)
0.946 1
 
< 0.1%
0.944 1
 
< 0.1%
0.914 1
 
< 0.1%
0.908 3
< 0.1%
0.906 1
 
< 0.1%
0.905 1
 
< 0.1%
0.902 2
< 0.1%
0.898 1
 
< 0.1%
0.89 1
 
< 0.1%
0.888 1
 
< 0.1%

acousticness
Real number (ℝ)

High correlation 

Distinct4707
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.096260315
Minimum1.07 × 10-6
Maximum0.988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.3 KiB
2025-03-19T12:14:50.703798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.07 × 10-6
5-th percentile8.9475 × 10-5
Q10.00173
median0.0164
Q30.107
95-th percentile0.497
Maximum0.988
Range0.98799893
Interquartile range (IQR)0.10527

Descriptive statistics

Standard deviation0.17084273
Coefficient of variation (CV)1.7747992
Kurtosis6.5271008
Mean0.096260315
Median Absolute Deviation (MAD)0.016194
Skewness2.5299953
Sum4129.1825
Variance0.029187238
MonotonicityNot monotonic
2025-03-19T12:14:50.763133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.128 75
 
0.2%
0.114 74
 
0.2%
0.106 69
 
0.2%
0.125 69
 
0.2%
0.0138 68
 
0.2%
0.105 61
 
0.1%
0.0105 60
 
0.1%
0.102 57
 
0.1%
0.111 57
 
0.1%
0.133 56
 
0.1%
Other values (4697) 42250
98.5%
ValueCountFrequency (%)
1.07 × 10-61
< 0.1%
1.14 × 10-61
< 0.1%
1.21 × 10-61
< 0.1%
1.46 × 10-61
< 0.1%
1.81 × 10-61
< 0.1%
1.83 × 10-61
< 0.1%
1.93 × 10-61
< 0.1%
2.14 × 10-61
< 0.1%
2.43 × 10-61
< 0.1%
2.46 × 10-61
< 0.1%
ValueCountFrequency (%)
0.988 1
 
< 0.1%
0.987 1
 
< 0.1%
0.986 2
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 2
< 0.1%
0.98 1
 
< 0.1%
0.978 3
< 0.1%
0.976 3
< 0.1%

instrumentalness
Real number (ℝ)

High correlation  Zeros 

Distinct4867
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28322936
Minimum0
Maximum0.989
Zeros11473
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size335.3 KiB
2025-03-19T12:14:50.819753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00596
Q30.722
95-th percentile0.909
Maximum0.989
Range0.989
Interquartile range (IQR)0.722

Descriptive statistics

Standard deviation0.37081022
Coefficient of variation (CV)1.3092224
Kurtosis-1.2495296
Mean0.28322936
Median Absolute Deviation (MAD)0.00596
Skewness0.75166777
Sum12149.407
Variance0.13750022
MonotonicityNot monotonic
2025-03-19T12:14:50.876455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11473
 
26.7%
0.871 93
 
0.2%
0.897 90
 
0.2%
0.901 85
 
0.2%
0.895 85
 
0.2%
0.858 79
 
0.2%
0.874 79
 
0.2%
0.865 78
 
0.2%
0.892 77
 
0.2%
0.883 77
 
0.2%
Other values (4857) 30680
71.5%
ValueCountFrequency (%)
0 11473
26.7%
1 × 10-65
 
< 0.1%
1.01 × 10-613
 
< 0.1%
1.02 × 10-613
 
< 0.1%
1.03 × 10-613
 
< 0.1%
1.04 × 10-616
 
< 0.1%
1.05 × 10-67
 
< 0.1%
1.06 × 10-613
 
< 0.1%
1.07 × 10-69
 
< 0.1%
1.08 × 10-67
 
< 0.1%
ValueCountFrequency (%)
0.989 3
< 0.1%
0.988 1
 
< 0.1%
0.987 1
 
< 0.1%
0.986 1
 
< 0.1%
0.985 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 6
< 0.1%
0.979 2
 
< 0.1%
0.978 4
< 0.1%

liveness
Real number (ℝ)

Distinct1737
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21408004
Minimum0.0107
Maximum0.988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.3 KiB
2025-03-19T12:14:50.933241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0107
5-th percentile0.0615
Q10.0996
median0.135
Q30.294
95-th percentile0.61825
Maximum0.988
Range0.9773
Interquartile range (IQR)0.1944

Descriptive statistics

Standard deviation0.17546346
Coefficient of variation (CV)0.81961618
Kurtosis3.0811814
Mean0.21408004
Median Absolute Deviation (MAD)0.0546
Skewness1.7803261
Sum9183.1773
Variance0.030787427
MonotonicityNot monotonic
2025-03-19T12:14:50.992258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 689
 
1.6%
0.109 487
 
1.1%
0.107 455
 
1.1%
0.11 443
 
1.0%
0.106 432
 
1.0%
0.105 368
 
0.9%
0.102 364
 
0.8%
0.114 349
 
0.8%
0.113 348
 
0.8%
0.115 316
 
0.7%
Other values (1727) 38645
90.1%
ValueCountFrequency (%)
0.0107 1
 
< 0.1%
0.0121 3
< 0.1%
0.0135 1
 
< 0.1%
0.0141 3
< 0.1%
0.0147 1
 
< 0.1%
0.0149 1
 
< 0.1%
0.0153 3
< 0.1%
0.0159 1
 
< 0.1%
0.0182 1
 
< 0.1%
0.0188 1
 
< 0.1%
ValueCountFrequency (%)
0.988 1
 
< 0.1%
0.981 2
 
< 0.1%
0.979 1
 
< 0.1%
0.978 3
< 0.1%
0.976 1
 
< 0.1%
0.975 5
< 0.1%
0.973 2
 
< 0.1%
0.97 1
 
< 0.1%
0.969 1
 
< 0.1%
0.968 1
 
< 0.1%

valence
Real number (ℝ)

Distinct1716
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3571699
Minimum0.0187
Maximum0.988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.3 KiB
2025-03-19T12:14:51.050021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0187
5-th percentile0.0436
Q10.161
median0.322
Q30.522
95-th percentile0.797
Maximum0.988
Range0.9693
Interquartile range (IQR)0.361

Descriptive statistics

Standard deviation0.23325546
Coefficient of variation (CV)0.65306582
Kurtosis-0.60890607
Mean0.3571699
Median Absolute Deviation (MAD)0.175
Skewness0.54957366
Sum15321.16
Variance0.054408108
MonotonicityNot monotonic
2025-03-19T12:14:51.110275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.142 99
 
0.2%
0.158 97
 
0.2%
0.144 93
 
0.2%
0.186 92
 
0.2%
0.141 91
 
0.2%
0.175 90
 
0.2%
0.15 88
 
0.2%
0.178 88
 
0.2%
0.152 87
 
0.2%
0.159 87
 
0.2%
Other values (1706) 41984
97.9%
ValueCountFrequency (%)
0.0187 2
< 0.1%
0.0196 3
< 0.1%
0.0199 1
 
< 0.1%
0.0206 3
< 0.1%
0.0218 3
< 0.1%
0.0228 1
 
< 0.1%
0.0235 1
 
< 0.1%
0.0236 1
 
< 0.1%
0.0237 1
 
< 0.1%
0.0239 2
< 0.1%
ValueCountFrequency (%)
0.988 1
 
< 0.1%
0.98 3
< 0.1%
0.979 1
 
< 0.1%
0.976 2
 
< 0.1%
0.975 4
< 0.1%
0.974 2
 
< 0.1%
0.973 1
 
< 0.1%
0.972 3
< 0.1%
0.971 4
< 0.1%
0.97 5
< 0.1%

tempo
Real number (ℝ)

Distinct15569
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.49209
Minimum57.967
Maximum220.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.3 KiB
2025-03-19T12:14:51.166956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum57.967
5-th percentile115.82175
Q1129.91875
median144.9705
Q3161.5975
95-th percentile192.8865
Maximum220.29
Range162.323
Interquartile range (IQR)31.67875

Descriptive statistics

Standard deviation23.861654
Coefficient of variation (CV)0.1617826
Kurtosis0.10894812
Mean147.49209
Median Absolute Deviation (MAD)15.455
Skewness0.47899078
Sum6326820.5
Variance569.37851
MonotonicityNot monotonic
2025-03-19T12:14:51.225966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150.02 59
 
0.1%
125 57
 
0.1%
149.998 51
 
0.1%
125.002 50
 
0.1%
128 49
 
0.1%
150 48
 
0.1%
149.992 47
 
0.1%
125.007 46
 
0.1%
123.999 46
 
0.1%
128.005 44
 
0.1%
Other values (15559) 42399
98.8%
ValueCountFrequency (%)
57.967 1
< 0.1%
61.309 1
< 0.1%
64.331 1
< 0.1%
64.934 1
< 0.1%
64.95 1
< 0.1%
66.424 1
< 0.1%
67.003 1
< 0.1%
69.986 1
< 0.1%
70.022 1
< 0.1%
70.231 1
< 0.1%
ValueCountFrequency (%)
220.29 1
< 0.1%
220.232 1
< 0.1%
220.216 1
< 0.1%
220.154 1
< 0.1%
220.152 1
< 0.1%
220.148 2
< 0.1%
220.138 1
< 0.1%
220.13 1
< 0.1%
220.112 1
< 0.1%
220.102 1
< 0.1%

type
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
audio_features
42896 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters600544
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaudio_features
2nd rowaudio_features
3rd rowaudio_features
4th rowaudio_features
5th rowaudio_features

Common Values

ValueCountFrequency (%)
audio_features 42896
100.0%

Length

2025-03-19T12:14:51.278506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T12:14:51.317433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
audio_features 42896
100.0%

Most occurring characters

ValueCountFrequency (%)
a 85792
14.3%
u 85792
14.3%
e 85792
14.3%
d 42896
7.1%
i 42896
7.1%
o 42896
7.1%
_ 42896
7.1%
f 42896
7.1%
t 42896
7.1%
r 42896
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 600544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 85792
14.3%
u 85792
14.3%
e 85792
14.3%
d 42896
7.1%
i 42896
7.1%
o 42896
7.1%
_ 42896
7.1%
f 42896
7.1%
t 42896
7.1%
r 42896
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 600544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 85792
14.3%
u 85792
14.3%
e 85792
14.3%
d 42896
7.1%
i 42896
7.1%
o 42896
7.1%
_ 42896
7.1%
f 42896
7.1%
t 42896
7.1%
r 42896
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 600544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 85792
14.3%
u 85792
14.3%
e 85792
14.3%
d 42896
7.1%
i 42896
7.1%
o 42896
7.1%
_ 42896
7.1%
f 42896
7.1%
t 42896
7.1%
r 42896
7.1%

duration_ms
Real number (ℝ)

High correlation 

Distinct26261
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250913.57
Minimum25600
Maximum913052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.3 KiB
2025-03-19T12:14:51.364084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum25600
5-th percentile121186
Q1179971.25
median224663
Q3301288.5
95-th percentile457973.25
Maximum913052
Range887452
Interquartile range (IQR)121317.25

Descriptive statistics

Standard deviation102979.76
Coefficient of variation (CV)0.41041926
Kurtosis0.49528913
Mean250913.57
Median Absolute Deviation (MAD)54250
Skewness0.95262368
Sum1.0763189 × 1010
Variance1.0604831 × 1010
MonotonicityNot monotonic
2025-03-19T12:14:51.418850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192000 105
 
0.2%
240000 87
 
0.2%
224000 68
 
0.2%
211200 57
 
0.1%
230400 57
 
0.1%
172800 52
 
0.1%
217600 49
 
0.1%
236800 43
 
0.1%
268800 43
 
0.1%
168000 39
 
0.1%
Other values (26251) 42296
98.6%
ValueCountFrequency (%)
25600 1
< 0.1%
35862 1
< 0.1%
38333 1
< 0.1%
41290 1
< 0.1%
42133 1
< 0.1%
43807 1
< 0.1%
48107 1
< 0.1%
48423 1
< 0.1%
48667 1
< 0.1%
49227 1
< 0.1%
ValueCountFrequency (%)
913052 1
< 0.1%
894386 1
< 0.1%
855502 1
< 0.1%
847302 1
< 0.1%
821168 1
< 0.1%
774294 1
< 0.1%
757972 1
< 0.1%
752000 1
< 0.1%
728413 1
< 0.1%
723573 2
< 0.1%

time_signature
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
4
40999 
3
 
1227
5
 
519
1
 
151

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42896
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 40999
95.6%
3 1227
 
2.9%
5 519
 
1.2%
1 151
 
0.4%

Length

2025-03-19T12:14:51.466303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T12:14:51.508134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4 40999
95.6%
3 1227
 
2.9%
5 519
 
1.2%
1 151
 
0.4%

Most occurring characters

ValueCountFrequency (%)
4 40999
95.6%
3 1227
 
2.9%
5 519
 
1.2%
1 151
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 40999
95.6%
3 1227
 
2.9%
5 519
 
1.2%
1 151
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 40999
95.6%
3 1227
 
2.9%
5 519
 
1.2%
1 151
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 40999
95.6%
3 1227
 
2.9%
5 519
 
1.2%
1 151
 
0.4%

genre
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Underground Rap
5948 
Dark Trap
4634 
Hiphop
3080 
trance
3038 
techhouse
3019 
Other values (10)
23177 

Length

Max length15
Median length10
Mean length7.6035761
Min length3

Characters and Unicode

Total characters326163
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDark Trap
2nd rowDark Trap
3rd rowDark Trap
4th rowDark Trap
5th rowDark Trap

Common Values

ValueCountFrequency (%)
Underground Rap 5948
13.9%
Dark Trap 4634
10.8%
Hiphop 3080
 
7.2%
trance 3038
 
7.1%
techhouse 3019
 
7.0%
dnb 3017
 
7.0%
trap 3014
 
7.0%
techno 3007
 
7.0%
psytrance 3003
 
7.0%
hardstyle 2981
 
6.9%
Other values (5) 8155
19.0%

Length

2025-03-19T12:14:51.555131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
trap 9632
17.4%
rap 7818
14.1%
underground 5948
10.7%
dark 4634
8.4%
hiphop 3080
 
5.6%
trance 3038
 
5.5%
techhouse 3019
 
5.4%
dnb 3017
 
5.4%
techno 3007
 
5.4%
psytrance 3003
 
5.4%
Other values (5) 9266
16.7%

Most occurring characters

ValueCountFrequency (%)
r 35184
 
10.8%
a 33090
 
10.1%
p 27081
 
8.3%
n 26089
 
8.0%
e 25999
 
8.0%
t 20046
 
6.1%
d 17894
 
5.5%
o 17227
 
5.3%
h 15106
 
4.6%
12566
 
3.9%
Other values (19) 95881
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 326163
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 35184
 
10.8%
a 33090
 
10.1%
p 27081
 
8.3%
n 26089
 
8.0%
e 25999
 
8.0%
t 20046
 
6.1%
d 17894
 
5.5%
o 17227
 
5.3%
h 15106
 
4.6%
12566
 
3.9%
Other values (19) 95881
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 326163
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 35184
 
10.8%
a 33090
 
10.1%
p 27081
 
8.3%
n 26089
 
8.0%
e 25999
 
8.0%
t 20046
 
6.1%
d 17894
 
5.5%
o 17227
 
5.3%
h 15106
 
4.6%
12566
 
3.9%
Other values (19) 95881
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 326163
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 35184
 
10.8%
a 33090
 
10.1%
p 27081
 
8.3%
n 26089
 
8.0%
e 25999
 
8.0%
t 20046
 
6.1%
d 17894
 
5.5%
o 17227
 
5.3%
h 15106
 
4.6%
12566
 
3.9%
Other values (19) 95881
29.4%

song_name
Text

Missing 

Distinct15439
Distinct (%)70.8%
Missing21085
Missing (%)49.2%
Memory size2.0 MiB
2025-03-19T12:14:51.715119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length138
Median length94
Mean length15.5058
Min length1

Characters and Unicode

Total characters338197
Distinct characters190
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11537 ?
Unique (%)52.9%

Sample

1st rowMercury: Retrograde
2nd rowPathology
3rd rowSymbiote
4th rowProductOfDrugs (Prod. The Virus and Antidote)
5th rowVenom
ValueCountFrequency (%)
feat 2494
 
4.0%
the 1698
 
2.7%
1655
 
2.6%
you 760
 
1.2%
me 759
 
1.2%
i 725
 
1.2%
a 589
 
0.9%
my 514
 
0.8%
it 500
 
0.8%
of 467
 
0.7%
Other values (10934) 52297
83.7%
2025-03-19T12:14:51.956053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40647
 
12.0%
e 29068
 
8.6%
a 19569
 
5.8%
o 17404
 
5.1%
t 16500
 
4.9%
i 15879
 
4.7%
n 13890
 
4.1%
r 12634
 
3.7%
l 10336
 
3.1%
s 10038
 
3.0%
Other values (180) 152232
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 338197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
40647
 
12.0%
e 29068
 
8.6%
a 19569
 
5.8%
o 17404
 
5.1%
t 16500
 
4.9%
i 15879
 
4.7%
n 13890
 
4.1%
r 12634
 
3.7%
l 10336
 
3.1%
s 10038
 
3.0%
Other values (180) 152232
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 338197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
40647
 
12.0%
e 29068
 
8.6%
a 19569
 
5.8%
o 17404
 
5.1%
t 16500
 
4.9%
i 15879
 
4.7%
n 13890
 
4.1%
r 12634
 
3.7%
l 10336
 
3.1%
s 10038
 
3.0%
Other values (180) 152232
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 338197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
40647
 
12.0%
e 29068
 
8.6%
a 19569
 
5.8%
o 17404
 
5.1%
t 16500
 
4.9%
i 15879
 
4.7%
n 13890
 
4.1%
r 12634
 
3.7%
l 10336
 
3.1%
s 10038
 
3.0%
Other values (180) 152232
45.0%

title
Text

Missing 

Distinct132
Distinct (%)0.6%
Missing21817
Missing (%)50.9%
Memory size2.1 MiB
2025-03-19T12:14:52.155336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length49
Median length37
Mean length22.348546
Min length4

Characters and Unicode

Total characters471085
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirtybird Players
2nd rowTech House Movement
3rd rowtech house
4th rowTech House Bangerz
5th rowtech house
ValueCountFrequency (%)
5310
 
7.0%
2020 4148
 
5.5%
trance 3894
 
5.2%
hardstyle 3072
 
4.1%
techno 3065
 
4.1%
bass 3014
 
4.0%
drum 2963
 
3.9%
psytrance 2637
 
3.5%
euphoric 2066
 
2.7%
house 2065
 
2.7%
Other values (142) 43109
57.2%
2025-03-19T12:14:52.409933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54284
 
11.5%
e 33420
 
7.1%
s 25916
 
5.5%
a 25025
 
5.3%
r 22909
 
4.9%
o 18765
 
4.0%
n 17752
 
3.8%
c 17585
 
3.7%
i 17407
 
3.7%
t 15676
 
3.3%
Other values (61) 222346
47.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 471085
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
54284
 
11.5%
e 33420
 
7.1%
s 25916
 
5.5%
a 25025
 
5.3%
r 22909
 
4.9%
o 18765
 
4.0%
n 17752
 
3.8%
c 17585
 
3.7%
i 17407
 
3.7%
t 15676
 
3.3%
Other values (61) 222346
47.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 471085
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
54284
 
11.5%
e 33420
 
7.1%
s 25916
 
5.5%
a 25025
 
5.3%
r 22909
 
4.9%
o 18765
 
4.0%
n 17752
 
3.8%
c 17585
 
3.7%
i 17407
 
3.7%
t 15676
 
3.3%
Other values (61) 222346
47.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 471085
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
54284
 
11.5%
e 33420
 
7.1%
s 25916
 
5.5%
a 25025
 
5.3%
r 22909
 
4.9%
o 18765
 
4.0%
n 17752
 
3.8%
c 17585
 
3.7%
i 17407
 
3.7%
t 15676
 
3.3%
Other values (61) 222346
47.2%

Interactions

2025-03-19T12:14:49.214212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:44.573190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.016219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.458839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.900020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.431413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.873956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.325168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.773921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.216876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.747957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.252612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:44.614184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.056244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.497328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.022825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.469242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.913316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.366502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.814616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.255937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.789666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.293390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:44.653427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.096586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.536187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.063245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.509266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.952688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.407431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.856207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.295661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.831065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.332185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:44.693714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.135603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.574542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.103241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.550757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.994883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.446433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.895051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.335053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.873072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.373793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:44.737080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.177697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.615072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.143673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.591849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.036496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.489113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.936056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.375829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.916412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.411967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:44.775576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.216008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.653572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.183352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.630031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.074482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.527518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.974800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.414927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.957703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.451817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:44.815449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.255263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.695095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.223659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.669045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.114845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.568191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.014208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.454385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.999739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.492711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:44.854678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.295877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.736488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.265198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.712629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.154678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.608607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.054754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.493671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.043327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.532525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:44.893102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.336188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.774866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.305865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.752887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.198759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.649026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.094072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.623320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.084952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.572762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:44.932481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.376162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.814878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.346685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.791733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.239411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.689765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.134067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.662728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.127735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.616424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:44.977085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.419548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:45.860346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.391082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:46.835794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.284773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:47.734025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.178240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:48.708916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-19T12:14:49.172667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-03-19T12:14:52.464682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
acousticnessdanceabilityduration_msenergygenreinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalence
acousticness1.0000.212-0.399-0.5340.166-0.508-0.021-0.077-0.1790.0260.2850.0910.0630.191
danceability0.2121.000-0.109-0.3720.243-0.141-0.009-0.232-0.2650.0860.255-0.2390.0740.358
duration_ms-0.399-0.1091.0000.3100.3230.5240.065-0.056-0.0500.080-0.295-0.1260.045-0.190
energy-0.534-0.3720.3101.0000.2270.3850.0370.2120.5680.036-0.0990.0210.060-0.050
genre0.1660.2430.3230.2271.0000.2930.0780.1180.2270.1690.1870.4200.0830.183
instrumentalness-0.508-0.1410.5240.3850.2931.0000.067-0.053-0.1410.033-0.429-0.2280.032-0.356
key-0.021-0.0090.0650.0370.0780.0671.000-0.011-0.0060.414-0.015-0.0100.0150.027
liveness-0.077-0.232-0.0560.2120.118-0.053-0.0111.0000.1830.0000.0730.0870.011-0.030
loudness-0.179-0.265-0.0500.5680.227-0.141-0.0060.1831.0000.0260.0750.2440.0460.083
mode0.0260.0860.0800.0360.1690.0330.4140.0000.0261.0000.0510.0500.0060.026
speechiness0.2850.255-0.295-0.0990.187-0.429-0.0150.0730.0750.0511.0000.1860.0510.272
tempo0.091-0.239-0.1260.0210.420-0.228-0.0100.0870.2440.0500.1861.0000.0650.055
time_signature0.0630.0740.0450.0600.0830.0320.0150.0110.0460.0060.0510.0651.0000.034
valence0.1910.358-0.190-0.0500.183-0.3560.027-0.0300.0830.0260.2720.0550.0341.000

Missing values

2025-03-19T12:14:49.678846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-19T12:14:49.800656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-19T12:14:49.912953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

danceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotypeduration_mstime_signaturegenresong_nametitle
00.8310.8142-7.36410.42000.05980.0134000.05560.3890156.985audio_features1245394Dark TrapMercury: RetrogradeNone
10.7190.4938-7.23010.07940.40100.0000000.11800.1240115.080audio_features2244274Dark TrapPathologyNone
20.8500.8935NaN10.06230.01380.0000040.37200.0391218.050audio_features988214Dark TrapSymbioteNone
30.4760.7810-4.71010.10300.02370.0000000.11400.1750186.948audio_features1236613Dark TrapProductOfDrugs (Prod. The Virus and Antidote)None
40.7980.6242-7.66810.29300.21700.0000000.16600.5910147.988audio_features1232984Dark TrapVenomNone
50.7210.5680-11.29510.41400.04520.2120000.12800.1090144.915audio_features1125114Dark TrapGattekaNone
60.7180.6688-4.16210.13700.02540.0078000.12400.0380130.826audio_features775844Dark Trapkamikaze (+ pulse)None
70.6940.7118NaN10.22100.03970.0000000.11200.2830138.049audio_features1275243Dark TrapT.R.U. (Totally Rotten Underground)None
80.7740.7511-2.44510.19800.06140.0000000.07280.1890219.960audio_features1403264Dark TrapI Put My Dick in Your MentalNone
90.8930.90711-10.40610.36700.15200.0311000.55800.3020199.942audio_features1219794Dark TrapAndromedaNone
danceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotypeduration_mstime_signaturegenresong_nametitle
428860.8750.7538-6.89900.09010.0405000.0000990.16800.7350112.002audio_features2101874Trap MetalSmoke BreakNone
428870.5890.7836-11.17010.04130.0004210.9160000.08390.0345123.995audio_features4103234technoNoneDark Techno Rave
428880.6230.8458-9.08210.05080.0160000.8640000.08620.3690134.978audio_features5467074tranceNoneTop Trance Songs EVER
428890.6740.9905NaN10.28400.3120000.0000160.62700.5090173.948audio_features1820694dnbNoneUKF Drum & Bass - All Uploads
428900.6530.9442NaN10.09170.0016300.4730000.35200.0754139.970audio_features2210004tranceNoneTRANNCE 2020 Best Trance Music Official
428910.8020.6309-9.34800.43200.2040000.0000020.06490.8770144.008audio_features667434Underground RapBang Bros!None
428920.5650.99611NaN00.06820.0000500.8160000.92200.0658145.000audio_features5908754psytranceNonePSYTRANCE BANGERS best new 2020
428930.5140.9550-5.18200.04020.0000530.8890000.33800.1100138.030audio_features2210074tranceNoneTop Trance Songs EVER
428940.7490.6391-9.84110.19400.0809000.6090000.36200.6490153.003audio_features2152024Underground RapSouthern HostilityNone
428950.6450.9800-6.89810.04450.0000290.8430000.31800.2600143.018audio_features4430774psytranceNonePsytrance: From Full on to Forrest Trance

Duplicate rows

Most frequently occurring

danceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotypeduration_mstime_signaturegenresong_nametitle# duplicates
4220.6310.6238-6.96910.26500.4630000.0000020.15100.7650160.460audio_features2278934HiphopEverything I AmNaN4
600.3890.7681-4.76510.25600.0380000.0000000.11900.334090.146audio_features1835674RapWRONG (feat. Lil Mosey)NaN3
980.4310.9612-6.59800.06030.0002460.2070000.36800.1630138.001audio_features2351874tranceNaNTRANNCE 2020 Best Trance Music Official3
1460.4730.73511-6.13100.04980.0106000.0000000.26600.6500154.546audio_features1794534EmoYou're So Last SummerNaN3
1500.4760.4074-9.23900.03680.2470000.0000000.14400.2260178.258audio_features2424674RnBOfficially Missing YouNaN3
1820.4950.8911-5.59900.05040.0018700.1120000.32200.2240149.982audio_features1424004hardstyleNaNEuphoric Hardstyle & Melodic Hardstyle3
2190.5130.8558-3.66410.03750.0293000.9330000.31600.0367173.996audio_features3641494dnbNaNLiquid Drum & Bass3
2850.5480.5549-6.40810.05870.2830000.0000000.07080.3820159.836audio_features2075074RnBStickwituNaN3
3010.5540.9784NaN10.13100.1110000.0000000.07310.5940135.095audio_features1626004EmoFaintNaN3
4190.6290.6961-5.57200.34800.0195000.0000000.05540.6230186.068audio_features2076274HiphopGold DiggerNaN3